| Document | Description |
|---|---|
| `Data Parallel Extension for Numpy*`_ | Documentation for programming NumPy-like codes on data-parallel devices |
| `Data Parallel Extension for Numba*`_ | Documentation for programming Numba codes on data-parallel devices as you program Numba on CPU |
| `Data Parallel Control`_ | Documentation how to manage data and devices, how to interchange data between different tensor implementations, and how to write data parallel extensions |
| `Intel VTune Profiler`_ | Performance profiler supporting analysis of bottlenecks from function leve down to low level instructions. Supports Python and Numba |
| `Intel Advisor`_ | Analyzes native and Python codes and provides the advice for better composition of heterogeneous algorithms |
| `Python* Array API Standard`_ | Standard for writing portable Numpy-like codes targeting different hardware vendors and frameworks operating with tensor data |
| `SYCL*`_ | Standard for writing C++-like codes for heterogeneous computing |
| `DPC++`_ | Free e-book on how to program data-parallel devices using Data Parallel C++ |
| `OpenCl*`_ | OpenCl* Standard for heterogeneous programming |
| `IEEE 754-2019 Standard for Floating-Point Arithmetic`_ | Standard for floating-point arithmetic, essential for writing robust numerical codes |
| `David Goldberg, What every computer scientist should know about floating-point arithmetic`_ | Scientific paper. Important for understanding how to write robust numerical code |
| `Numpy*`_ | Documentation for Numpy - foundational CPU library for array programming. Used in conjunction with `Data Parallel Extension for Numpy*`_. |
| `Numba*`_ | Documentation for Numba - Just-In-Time compiler for Numpy-like codes. Used in conjunction with `Data Parallel Extension for Numba*`_. |